Intravenous bamlanivimab use associates with reduced hospitalization in high-risk patients with mild to moderate COVID-19

  1. Ravindra Ganesh
  2. Colin F. Pawlowski
  3. John C. O’Horo
  4. Lori L. Arndt
  5. Richard F. Arndt
  6. Sarah J. Bell
  7. Dennis M. Bierle
  8. Molly Destro Borgen
  9. Sara N. Hanson
  10. Alexander Heyliger
  11. Jennifer J. Larsen
  12. Patrick J. Lenehan
  13. Robert Orenstein
  14. Arjun Puranik
  15. Leigh L. Speicher
  16. Sidna M. Tulledge-Scheitel
  17. A.J. Venkatakrishnan
  18. Caroline G. Wilker
  19. Andrew D. Badley
  20. Raymund R. Razonable

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1172/jci151697
  2. ScreenIT

    SciScore for 10.1101/2021.05.23.21257670: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIRB: Study Design and Participants: This retrospective study was approved by the Mayo Clinic Institutional Review Board.
    Consent: Informed consent was waived and patients without research authorization were excluded.
    Sex as a biological variablenot detected.
    RandomizationFor each of the patients with cancelled bamlanivimab orders, the relative infusion dates were randomly sampled from the distribution of relative infusion dates for the actual bamlanivimab-treated cohort, ensuring that the infusion dates were within the study period.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analysis was performed with the aid of the “scipy” package (v0.25.6) in Python.22 Hospitalization-free survival was also assessed at daily intervals with a Kaplan-Meier analysis and a corresponding log-rank test.
    Python.22
    suggested: None
    24 Mixed ANOVA tests were performed using the “pingouin” (v0.3.11) package in Python, with the Greenhouse-Geisser correction applied afterwards to adjust for violations of the sphericity assumption.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.05.23.21257670v1 on medRxiv